This invention relates to the automated characterization and identification of objects, including automated detection of their borders, in intravascular ultrasonic imaging.
The value of ultrasonic imaging can be enhanced if models can be developed which accurately correlate properties of ultrasound objects in an in-vivo environment. Heretofore there have been few automated approaches in the field of in-vivo ultrasonic object definition and identification. Previously proposed approaches may be classified in two categories. First, the defining of an object as an area surrounded by a detected border. Detection of the border in turn is based on local properties and behavior of the border. Second, the development of a theoretical model for an ultrasound object which is validated for in vitro studies.
According to the first category, approaches have been developed at the Thoraxcenter in Rotterdam, Holland, and at the University of Iowa which employ feature extraction techniques for border detection. In those approaches an object is defined as the area encompassed by a detected border, and the algorithms used are optimized to provide the best possible border. These approaches are limited because algorithms provide little information about the parameters characterizing the object under observation. Neither can the algorithms adapt their behavior in accordance with frame-to-frame variants in object properties. In addition, the algorithms are computational and time intensive in cross-sectional area computation, since they must completely calculate the object border in each frame of the volume.
In the second category of approaches, tissue modeling techniques have been developed for comparing data patterns with predefined models, e.g., at the Stanford Center for Cardiac Interventions and the University of Texas. In these types of techniques, a consistent tissue behavior is assumed which can be modeled. The models describe internal properties of an object which can be used to identify the object. However, such models are inherently limited in that by their nature they, cannot accommodate variations in object properties from patient to patient, or even from frame to frame. A paper by Petropulu et al. entitled MODELING THE ULTRASOUND BACKSCATTERED SIGNAL USING xcex1-STABLE DISTRIBUTIONS, 1996 IEEE Ultrasonics Symposium, p. 103 is representative of the model-based approach. Therein certain assumptions about theoretical statistical behavior are made, and the assumptions are used to identify the object in an in-vivo case study. This limited approach is subject to significant errors because it yields a model which only partially describes the object behavior and does not take into account variations from case to case.
Most known techniques for object border detection use a purely manual method for border tracing, which is done simply by drawing the boundary of the object. This procedure is slow and is subject to errors and variations between users. Moreover, it does not allow for the characterization of the object within the border.
One known description of a combination of different approaches is Spencer et al., CHARACTERISATION OF ATHEROSCLEROTIC PLAQUE BY SPECTRAL ANALYSIS OF 30 MHZ INTRAVASCULAR ULTRASOUND RADIO FREQUENCY DATA, 1996 IEEE ULTRASONICS SYMPOSIUM, p. 1073, wherein a statistical model is developed from in-vitro studies, then applied to in-vivo cases. Such an approach is limited by both the differences between in-vitro and in-vivo conditions and between in-vivo cases.
What are needed are better techniques for border detection and for identifying and characterizing objects and features of ultrasonic imaging.
The invention provides exemplary systems and methods for evaluating objects located within ultrasonic images. According to one exemplary method, in-vivo ultrasound image data is obtained and an image is constructed from the data which includes at least one object. At least two parameters are calculated from the data for selected locations within the object. These parameters are representative of the intensity of the object and the spacial structure of the object.
Preferably, the data that is collected is time-domain data. This data is transformed into frequency-domain data and compressed. The two parameters preferably comprise the zero frequency magnitude of the compressed frequency-domain data and the sum of the frequency magnitudes of the compressed frequency-domain data. Use of these two parameters is particularly advantageous in that they may be used to characterize a physical object within a patient. For example, the zero frequency magnitude of the compressed frequency-domain data is representative of the physical composition of the physical object, e.g., its hardness, and the sum of the frequency magnitudes of the compressed frequency-domain data is representative of the structure of the physical object. Hence, the invention provides a way to obtain patient specific parameters in a in-vivo processes. Further, these parameters represent various physical characteristic of the object under evaluation so that a treatment may more carefully be tailored.
Moreover, these parameters may be saved and kept as part of the patient""s history so that they may be compared to parameters calculated after one or more treatments of the object.
In another exemplary method, in-vivo ultrasound image data is provided in a plurality of frames. An object is identified within each image by moving a region of interest to different locations in the image and evaluating object identifying parameters at the different locations to determine if the parameters fall within an acceptable range that are indicative of the object. The area of the object within each of the frames is then computed based on the area of the locations having the parameters which fall within the acceptable range. The areas of two adjacent frames are then compared to determine if the difference between the two areas exceeds a predetermined amount. If so, the area of one of the adjacent frames is recomputed using different criteria.
For example, the range of acceptable object identifying parameters may be varied when recomputing the area of one of the adjacent frames. As another example, a starting location of the region of interest may be varied when recomputing the area so one of the adjacent frames. As still another example, the size of the region of interest may be varied when recomputing the area of one of the adjacent frames. In the event that the difference between recomputed area and the area of the object in the adjacent frame still exceeds the predetermined amount, a message may be produced indicating the discrepancy.
In one specific embodiment, a method is provided for evaluating an object within an ultrasound image that is constructed from time-domain data. According to the method, a region of interest within the object is selected for observation. At the selected region of interest, a transformation of the time-domain data is performed to obtain frequency-domain data. The frequency-domain data is then compressed or filtered, and object identifying parameters are obtained from the compressed frequency-domain data. Multiple definition regions of interest which are subsets of the selected region of interest are then defined. Preferably, the definition regions of interest are proportional in shape to the selected region of interest and are located at a distinct locations within the selected region of interest. A transformation of the time-domain data defining the definition regions of interest is then performed to obtain frequency-domain data that is representative of the definition regions of interest. From this data, a range of acceptable object identifying parameters is obtained.
Once this range has been determined, definition regions of interest are positioned at selected locations in the ultrasound image, and transformations of the time-domain data are performed to obtain frequency-domain data representative of the definition regions of interest in the ultrasound image. Object identifying parameters from this frequency-domain data are then obtained. These object identifying parameters are then evaluated to determine if they are within the range of acceptable object identifying parameters that was previously calculated. The selected definition regions of interest in the ultrasound image which have object identifying parameters which fall within the acceptable range are then marked or flagged so that an object boundary may be constructed around the flagged definition regions of interest. Once the boundary is constructed, an area of the object may easily be calculated.
In one particular aspect, the data is compressed by evaluating only the data which has a spectral power content below a selected fractional threshold. In another aspect, the object boundary and the object are displayed (such as on a display screen) to allow a user to indicate whether the object boundary acceptably bounds the object. If the constructed boundary is inaccurate or otherwise unacceptable, a new boundary may be constructed in one of two ways. In one way, the user may select another region of interest (e.g., by utilizing a mouse to move the region of interest to another location on the displayed object), and repeating steps of the method with the new region of interest. Alternatively, the data may be compressed or filtered in a different manner, and then repeating the steps of the method.
Typically, the ultrasound image is defined by multiple frames of time-domain data, and the object boundary is constructed in one of the frames (conveniently referred to as a first one of the frames). Another one of the frames is then selected and an object boundary is constructed around the object in the second frame and an area is calculated. This process is repeated for each frame having the object. Hence, one advantage of the invention is that the area of the object in subsequent frames may proceed with essentially no user interaction. Once the areas have been calculated, a volume of the object may be computed based on the areas of the objects in the frames and the distances between the frames.
In one aspect, the object boundary around the object in the second and subsequent frames are constructed by placing a definition region of interest at a center of mass of the object as determined prom the first (or a previous) frame and repeating the steps that follow the determination of the range of acceptable object identifying parameters.
In one particularly preferable aspect, the area of the object in the first frame and the second frame are compared to determine if the areas differ by more than a predetermined amount. If so, the area of the object in the second frame is recomputed using varied criteria. For example, the starting point of the definition region of interest in the object of the second frame may be adjusted. Alternatively, the size of the definition region of interest may be changed. Further, the range of acceptable object identifying parameters may be varied.
The invention will be better understood by reference to the following detailed description in connection with the accompanying drawings.